SPSS Advanced Statistical Analysis Training Overview
This Advanced Statistical Analysis using SPSS training course teaches attendees more advanced SPSS® regression and analysis techniques. Students learn when and how to use each approach and how to interpret the results.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Master various analytic techniques in SPSS, including Discriminant, Survival, Cluster, Factor, Loglinear, Multivariate, and more
- Perform Binary and Multinomial logistic regression
- Understand when to apply each approach based on the characteristics of your data and the analytic outcomes you are seeking
Prerequisites
All students should have taken Introduction to SPSS or have equivalent experience.
Outline
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Introduction and Overview
- Goals of the Course
- Taxonomy of Methods
- General Approach
Discriminant Analysis
- How Does Discriminant Analysis Work?
- The Elements of Discriminant Analysis
- The Discriminant Model
- How Cases are Classified
- Assumptions of Discriminant Analysis
- A Two-Group Discriminant Example
- Checking Variance Assumptions
- How Does Discriminant Analysis Work?
- The Elements of Discriminant Analysis
- The Discriminant Model
- How Cases are Classified
- Assumptions of Discriminant Analysis
- A Two-Group Discriminant Example
- Checking Variance Assumptions
- Running a Discriminant Analysis
- The Discriminant Coefficients
- Classification Statistics 2- 18 Prediction
- The Assumption of Equal Covariance
- Modifying the List of Predictors
- Casewise Statistics and Outliers
- Adjusting Prior Probabilities
- Validating the Discriminant Model
- Stepwise Model Selection
- Three-Group Discriminant Analysis
Binary Logistic Regression
- How Does Logistic Regression Work?
- The Logistic Equation
- The Elements of Logistic Regression
- Assumptions of Logistic Regression
- A First Example of Logistic Regression
- Interpreting Logistic Regression Coefficients
- Making Predictions
- The Accuracy of Prediction
- Estimated Probabilities
- Checking Classifications
- Residual Analysis
- Stepwise Logistic Regression
- Summary
- Comparison to Discriminant Analysis
Multinomial Logistic Regression
- Multinomial Logistic Model
- Assumptions of Multinomial Logistic Regression
- A Multinomial Logistic Analysis: Predicting Credit Risk
- Interpreting Coefficients
- Classification Table
- Making Predictions
- Appendix: Multinomial Logistic with a Two-Category Outcome
Survival Analysis (Kaplan-Meier)
- What is Survival Analysis
- Concepts
- Censoring
- What to Look for in Survival Analysis
- Survival Procedures in SPSS
- An Example: Kaplan-Meier
- Results
- Extensions
Cluster Analysis
- How Does Cluster Analysis Work?
- Types of Data Used for Clustering
- What to Look at When Clustering
- Methods
- Distance and Standardization
- Overall Recommendations
- Hierarchical Cluster Analysis
- Cluster Results
- Obtaining Mean Profiles of Clusters
- Relating Clusters to Other Variables
- Summary of First Cluster Example
- Example II: K-Means Clustering
- Running K-Means Clustering
Factor Analysis
- Uses of Factor Analysis
- What to Look for When Running Factor Analysis
- Principles
- The Idea of a Principal Component
- Factor Analysis Versus Principal Components
- Number of Factors
- Rotation
- Factor Scores & Sample Size
- Methods
- An Example: 1988 Olympic Decathlon Scores
- Looking at Correlations
- Principal Components Analysis with an Orthogonal Rotation
- Principal Axis Factoring with an Oblique Rotation
Loglinear Analysis
- What are Loglinear Models
- Relations Among Loglinear, Logit Models and Logistic Regression
- What to Look for in Loglinear and Logit Analysis
- Assumptions
- Procedures in SPSS that Run Loglinear or Logit Analysis
- Example: Analysis of Location Preference (Model Selection)
- Running the Analysis
- Significance Tests
- Coefficient Interpretation
- Summary
- Logit Analysis with Specific Model (Genlog)
- Results
Multivariate Analysis of Variance
- Why Perform MANOVA
- Assumptions of MANOVA
- What to Look for in MANOVA
- SPSS Version 7 Differences
- An Example: Memory Influences
- Examining the Output
- Post Hoc Tests
- Post Hoc Testing of Means
Repeated measures Analysis of Variance
- Why do a Repeated Measures Study
- The Logic of Repeated Measures
- Assumptions
- Example: One Factor Drug Study
- Examining Results
- Further Analysis
- Planned Comparisons
- Ad Viewing with Pre-Post Brand Ratings
- Examining Results
- Tests of Assumptions
- Profile Plots
- Extensions
Conclusion
Training Materials
All SSPS training attendees receive an extensive library of SPSS examples to take with them following the training.
Software Requirements
This class uses SPSS Statistics 25 or newer but is appropriate to SPSS Statistics 19 and newer. Attendees are assumed to have their own copy of the software.
